Agentic AI: Building Data-First AI Agents
Agentic AI: Building Data-First AI Agents
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 43m | 190 MB
Instructor: Morten Rand-Hendriksen
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 43m | 190 MB
Instructor: Morten Rand-Hendriksen
In this course, Senior Staff Instructor Morten Rand-Hendriksen and Microsoft Director of Data & AI Strategy Viroopax Mirji analyze the data challenges enterprises face when building AI agents. The course examines data quality, privacy, integration, and bias, with real-world examples from industries like healthcare and autonomous vehicles. The conversation covers best practices for data management, governance, and transitioning to an AI-driven future, ensuring transparency and accountability in AI systems.
Learning objectives
- Analyze the biggest data challenges enterprises face when building AI agents.
- Examine critical aspects of data management, including data quality, privacy, integration, and bias.
- Illustrate the impact of poor data management on AI projects with real-world examples.
- Learn best practices for ensuring high-quality data, including data cleaning, transformation, and feature selection.
- Explore the importance of data governance and its key principles.
- Compare AI-centric data governance to traditional data governance.
- Provide strategies for transitioning to an AI agent-driven future.
- Understand the data challenges in AI implementation and the strategies to overcome them.
Agentic AI: Building Data-First AI Agents
